Data Monetization

Seizing Opportunities

Mobile networks have transformed from providing simple voice services, to enabling an ever-growing and vast ecosystem of primarily pre-RCS, over-the-top services. They now face the significant hurdle of capturing, processing, and analyzing network- and subscriber-usage data in order to maximize value.

Know the Customer

Declining revenue-per-bit and competition from non-traditional players has challenged service providers to diversify their revenues, prevent revenue leakage, and build a more competitive advantage through innovation.

One of the ways this is accomplished is through the development of unique, personalized services that demand a price premium and are true differentiators in the market. This drives the need for data monetization initiatives where service providers can better understand and harness the opportunity presented in subscriber data. However, service providers, with limited resources for new data insight systems, need a solution for monetization that breaks the linear relationship between subscriber-data growth and solution cost, to ensure a proper ROI.

Get Smart

VIAVI Solutions has significant experience managing the transition to new network technologies and the adoption of new services. We have a deep understanding of how the networks and the services work. This experience has informed the development of efficient monitoring and test solutions that give service providers the visibility they need in order to plan, deploy, operate, monitor, and optimize networks and services.

With a fundamentally new approach to handling big data, VIAVI’s new “SMARTanalytics” solutions are designed to cost-effectively unlock the potential inherent in big data so that opportunities are captured and monetized.  

VIAVI has a broad portfolio of solutions that enable big data monetization through SMARTanalytics.

These solutions are:

  • Synchronized: data is auto-correlated across sources and services 
  • Meaningful: with resulting analytics that are immediately relevant and actionable to the application that uses them
  • Adaptive: analytics dynamically change to account for new data sources as well as for new needs by the downstream applications
  • Real-Time: with analytics that is available within seconds

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